The Future of Restaurants in an AI World: Hudson Smith with Olo Founder Noah Glass

Speaker A: And I'll say as a founder and CEO of a software company that I've been running for over 20 years, I feel like I have been tapped into the rising top gun class. Have Orlando Bravo in the front row maverick as my guide.

Speaker B: Welcome to Thoma Bravo's behind the Deal. I'm um, Orlando Bravo, founder and managing partner at Thoma Bravo. For today's episode we're going back to our 2025 AI summit where we brought together industry leaders to discuss innovation and the future of technology. What you're about to hear is one of those conversations recorded live at the event. In this session, Thoma Bravo partner Hudson Smith sits down with Noah Glass, founder and CEO of Olo, a leading restaurant technology platform serving more than 750 enterprise brands. Together they discussed how Noah built Olo into a category leader, why the company was so compelling to us, and why thoma bravo taking Olo private in September 2025 created a great foundation for its next phase of growth. It's a story of a founder who survived and built his business through multiple technology eras. And a conversation about how AI could help power the restaurant of the future. I'll pass the mic to them.

Speaker C: One of my favorite things about working in private equity in Toba, Bravo is working with founders. Um, and what is extremely rare is to found a founder who started a company right out of college and has now scaled it um, as a ah, growth equity backed company, then as a public company and now as a private equity company. So very rare. Excited to introduce you all to Noah. His story is incredible. Um, in coming out of Yale he uh, moved to Wall street um, 22 years ago, uh, 2003 with a few college friends and he was frustrated waiting in line for coffee in the morning. Everyone coming from Wall street going to get coffee at 9am, uh going into work. Uh, he's a very efficient guy obviously, uh, pushes very hard. Uh, and he said geez, like you know in high school I worked as a pizza delivery guy and as a cashier. He really understood restaurants and you know, how they do it and said gosh, it'd be a lot better if you could actually order this via text. And he was an early adopter of a Palm Pilot. Uh, the iPhone wasn't out yet, uh, that wasn't out for I think two more years. And uh, so he met some developers while he was in South Africa uh, and got them to build a prototype of uh, a simple text ordering solution. Uh, he happened to meet an investor. Uh, the investor said hey, if you're Willing to quit your job and go at this full time, I'll give you $500,000 and get going. Um, and so he launched, uh, effectively olo. It was named something else in the beginning, um, as this text message ordering business, um, at a time that was very early, similar to where AI is. It was very early in the um, transition to mobile phones, um, with only 5% of the population I think at the time having phones. But Noah was early and said, hey, I want to get in front of this and people will start using their phones for this. 16, uh, years later, he's a market leader. Um, he took the company public in 2021 right out of COVID when restaurants were desperate to change their processes and embrace technology. Um, was growing incredibly rapidly. Um, started off as a public company in that period, uh, and we started tracking Noah in 2022, uh, when the stock market corrected a bit. Um, growth had slowed a little bit post the COVID boom. Um, but he consistently, every two to three quarters we called him and he said he loved being a public company. Um, he loved his board situation. Um, and he wanted to keep going. Um, but we kind of convinced him that hey, there's another way of creating value. Um, in the private markets you'll have currency to do M and A, which hopefully we'll get done soon. Um, we've got, you know, you can get to much higher margins because what the public markets were focusing them on weren't necessarily the right things and much faster decision making than this public board. So we finally got the OLO deal done and so that's a little bit of uh, Noah's background. So before we have Noah come up, wanted to share a quick video of what OLO does. Uh, Noah's vision for the company.

Speaker D: What will the restaurant of the future look like? Perhaps a better question is what will guests expect in the years ahead? As I look at the landscape today,

Speaker E: I know there is so much more

Speaker D: work to be done. With every interaction, our restaurant brand customers get one step closer to having a 360 degree view of each guest, including purchase behavior, dietary restrictions, preferred payment method, favorite marketing channel, lifetime value and more. The service enhancements and guest insights gained by harnessing every transaction have become a key competitive advantage for OLO restaurant brand customers. So when we imagine the restaurant of the future, an OLO power drive thru will immediately identify you by your license plate. A personalized greeting will appear on the screen and ask if you'd like to order the hamburger you got last time or try the chicken tenders. A data driven recommendation based on the purchase history and dietary preferences of guests just like you. As we think about the Dine in experience, we imagine a reality where you're escorting to your preferred table where your favorite wine awaits and though you've never met this host, she wishes you a, uh, happy anniversary. A server with a handheld ordering device suggests the filet mignon with sauteed mushrooms and without rosemary based on the past orders, likes and dislikes in your guest profile, and the wine list has been personally curated based on your preference for Italian reds and uh because your payment method is securely saved after dining, you're free to get up and go without waiting for the check. The borderless account tied to your reservation already took care of your payment and tip using your default settings. A follow up text reminds you you can adjust your tip within 24 hours in those moments when making dinner isn't an option. What if you got a perfectly timed push notification from your local pizza restaurant, prompting you to place a pickup order for your favorite vegan pizza for $5?

Speaker A: Off.

Speaker D: With just a handful of taps, your order is fired off to the kitchen. Once you park at the curb, a Bluetooth beacon detects the huevor eye, and within two minutes a runner is at your car with your pizza. The restaurant knows you're in a white Mazda, thanks to your saved settings. An automated survey arrives in your inbox two hours later, prompting you to review your experience in exchange for a free dessert with your next order. Lunch with coworkers is less than perfect when you're waiting for a table, waiting for a menu, waiting to place an order, and managing to take only a few bites before rushing to get back to the office when you can order and pay at the table, controlling the entire dining experience. That allows lunch to be a little more about lunch rather than the struggle to get it. And when you're ready for dessert, use your phone to scan the QR code on the table and order one of the custom selected suggestions. If you want to split the check or add a coffee to go, we think that should be quick and easy too. When you want to order in for a group of friends with varying dietary preferences, what if you could share the link to your chosen restaurant with the group, allowing them to order whatever they want and taking you out of the middle? Once the unique link is shared, everyone can place their order with the guesswork ticket out of the equation. Checkout is seamless and the food is scheduled to be delivered just on time. When food is on the way, you get real time information on location and pta. And after the delivery, you get an optional tech survey so you can rate the entire experience. What if, instead of waiting in line, you could simply head straight to a kiosk? As you approach, you're automatically logged into your account using facial recognition that you previously approved. After the instant verification, your order history and favorites pop up, making the order entry process fast and easy. Once you place your order, you get an alert on your phone with your receipt and wait time. After a few minutes, your order, your meal is ready, and you're on your way. Leveraging the ideal blend of, uh, OLO products and integrations, restaurant brands of all sizes can build solutions that help both their front and back of house staff operate as productively and profitably as possible, while increasing hospitality, not eroding it with a growing network of more than 84,000 restaurant locations and 85 million guests.

Speaker C: And now, Noah Glass. Uh,

Speaker E: Thanks, Mick.

Speaker A: Yeah, I just wanted to start out with, uh, some gratitude, um, on behalf

Speaker E: of all of Team olo.

Speaker A: I am so thrilled that we are

Speaker E: part of this community and part of

Speaker A: the Thoma Bravo portfolio. Um, and I'll say, as a founder and CEO of a software company that I've been running for over 20 years, I feel like I have been tapped into the rising Top Gun class. I have Orlando Bravo in the front row, Maverick as my guide. Um, but really, I mean, I have

Speaker E: taken so much from this event.

Speaker A: Uh, every interaction that I've had, every

Speaker E: session that we've had has been so enlightening to me. And one of the things that I've

Speaker A: always felt about our investors is they really are the wind beneath our wings. And I want to extend my gratitude to this group because of course, as

Speaker E: investors in Thoma Bravo, you are the

Speaker A: wind beneath Thoma Bravo's wings. And I am just so thrilled to be on this journey that we're on together. So thank you.

Speaker C: Well, uh, with that, let's jump right in and understand the impact of AI. Um, so the idea for olo, as we talked about it, was text ordering system very early, um, in the mobile phone time period. And I guess it's kind of similar to where we are with AI in a way. And I wonder, are we in kind of the text ordering phase of AI and it's going to evolve into that vision over time in the AI world. What are your thoughts on that?

Speaker E: I think that's right. I like the question of what inning are we in? And I always say to our team, we're not even getting out of the dugout yet. It is so, so early in the space. I think that's true in general for our opportunity, and I certainly think it's true of AI in the restaurant industry. Um, text message ordering is not the interface that we know today, but it was directionally right. It was the thing that showed restaurants that we were on their side and that we were trying to help them to solve a business problem that they experienced, increasing their throughput capacity and their accuracy and making for a better guest experience. And I think the same thing can be said of AI and where it's showing up in our platform today and around the industry today.

Speaker A: It's very early.

Speaker E: It's probably showing up in some ways that we'll look back on and be embarrassed by, like text message ordering. What our industry does is it makes people feel seen and feel special and there's a limit to what humans can do. I love what Tom said about hybrid intelligence earlier. That's really how I've seen what our platform can do in enriching human delivered hospitality. There's a rule called Dunbar's number which holds that you can only really have about 150 human relationships in your brain. Um, and I think the magic of AI and how we're applying it at OLO is to augment those human limitations with that hybrid intelligence and help restaurants make every guest feel like a regular. And that's why our mission is hospitality at scale. What we're scaling is a restaurant's ability to make people feel seen and special and to have a personalized experience every single time they walk into the restaurant.

Speaker C: And you know, we spend a lot of time in diligence as software experts. It's our job during diligence on a new platform, Making a big bet on this vertical, on this space, and on you. And your technology is really understanding the m moats of what is hard about what OLO does. Um, and then layering AI on top of that as that changes what moats do we have, what defenses do we have, um, where we can get comfortable that, hey, over the next five years this can be a great growing company, um, and get there. So maybe talk about some of the moats we talked about during diligence and probably ask, uh, too many questions about,

Speaker E: well, I read the full 160 page diligence report that you all did, and I was glad that it found some of the things that we've always held to be great moats about our business and specifically in today's age, moats against AI or agentic competitors. Um, one big thing is just how structured a restaurant order is. You can't just say, I want a coffee. You need to say what size of coffee you want, how strong you want the coffee to be, how much sugar you want, or no sugar at all. What kind of milk you want, how much milk you want. There are a lot of different attributes to an order, so many different permutations. And that's a very simple example. Multiply that by the scale of a restaurant, uh, chain menu, and all the different modifiers and substitutions and replacements that you can do on an order. That structured data, uh, I think a lot of people have brought up, is one very complex part of our business that from the outside looking in, you can overlook. You can say, oh, that seems pretty easy. A coffee order is actually very complicated to get it right in what the guest wants when the kitchen or the barista is making that order exactly to their specification. So that's number one. Um, the other thing is that we do this for over 750 enterprise barista brands, um, now about 90,000 individual restaurant locations, and we're the exclusive provider for those brands. So we have an inherent moat in being the exclusive digital ordering provider. And the thing that I may be most excited about now is taking it to the next level of having the motive of the network, so the guest having a relationship. There was a brief mention in that video of a borderless account, or what we now call an OLO account, where the guest can have an account at the OLO platform level, and then we can see their use of the platform across all of those different restaurant brands. Um, that is an incredible moat that we have of resolving the guest identity. It could be their very first time in a restaurant. But we can bring all the context of how we've observed that guest in other restaurants to bear in that interaction with the restaurant right there and then.

Speaker C: Yeah, similar to Shopify for. For retail. Um, yeah. And I think maybe we could talk a little bit about the differentiated data, too, because one of the things, as we all have experienced restaurants, everyone in this room has opinions on it. Um, but they don't get a lot of the data because they're using across chains, different POS systems. And we had great data on this from you and others, um, as to how fragmented their POS systems are. And then even the POS system, if you think about it, it's not getting the detailed data you just mentioned, because. Because a lot of times it doesn't really know who the customer is. It's just a credit card at a Chipotle for Example, it doesn't know what you really built in your custom order. If you went through the line in the store, um, it just knows whether you got avocado because that's an extra $1.50 or whatever. And so that's all the data they get versus you. Since you came at it from the digital online ordering. You have to capture all of that information, you have to structure all of it. Maybe talk a little bit about just that. Do the restaurants understand the data that you have? Um, and then hopefully can apply it maybe to the POS side.

Speaker E: Yeah. If I could just take a step back on the restaurant industry. I've done this a lot over the last four and a half years as a public company CEO of retraining people in what is the restaurant industry and

Speaker A: how does it work?

Speaker E: Um, I heard Orlando on a CNBC

Speaker A: interview recently talk about software as a,

Speaker E: uh, 1.5 trillion trillion dollar industry. It's actually coincidental, but the US restaurant

Speaker A: industry, just the US is a $1.5 trillion industry alone.

Speaker E: It is a massive, massive industry.

Speaker A: It is also a really.

Speaker E: From Arvind's commentary last night about population and productivity. It is a very unproductive industry in that the revenue per full time employee is very, very low. I can't think of an industry where it's lower. And it's an industry where restaurants are really data poor and they don't know who their guests are. So when we romanticize restaurants, we think about the experience of cheers. You walk in, they know your name, your drink, and your bar stool.

Speaker A: But that doesn't really scale. People can't do that.

Speaker E: They can't hold more than 150 relationships in their mind. And restaurants don't know their guests. Fundamentally, the very best restaurants have maybe 15% of their guests, 20% of their guests in some trackable format where they can link an order back to a guest identity. Um, what we're doing to your point is changing all of that. So if you think about restaurants again for one moment, it's only 25% of industry transactions that happen inside the four walls of the restaurant and are consumed inside the four walls of the restaurant. That's not how most people think about the industry. Three out of every four transactions are food that is consumed outside the four walls of the restaurant. And that is really what our strong suit is. Every one of the orders coming through our platform, we know who the guest is. And if they've ordered before, we know their order history. And so that brings into the equation for restaurants for the very first time, a whole lot of data that enables them to do what they were setting out to do, know who the guest is, make them feel special and seen, and then personalize the experience and deliver on hospitality. So we are bringing that into restaurants and now with how we've moved into payments, we're able to not just see the transactions that are going through our platform, we're able to see all the transactions that are happening off of our platform and tie those back to a guest identity.

Speaker A: So for the very first time, and

Speaker E: I've heard restaurants, CMOs talk about this as well, that's the holy grail for the industry, 100% of transactions can get resolved back to a guest identity. And we can see about an individual guest, all of the transactions that they place, both those that are digital transactions for delivery, for takeout, for drive through, and for those on premise occasions. So that is a, uh, huge unlock for our industry. And it's the setup for an incredible tailwind that AI then represents.

Speaker C: Sure, yeah. And another moat that wasn't as powerful as the ones you mentioned was also just how aligned and partnered you are with the restaurants where when you think about this ecosystem, you've got these marketplaces like uber Eats and DoorDash who are owning the consumer experience, working with all of these different restaurants. Um, and they're providing some services to the restaurants, but really no visibility there versus you sitting in the middle, really giving that data back to the restaurant and allowing them make better talk. Uh, to me, that made me more comfortable with just the alignment that you have as things change, as AI comes in, you're going to be the partner to the restaurant versus some other players that have their own business and their own motivations to own the consumer experience.

Speaker E: It's something that we say all the time as a rallying cry at olo, that we are on the restaurant side. And the reason that we say this, we're sort of an outsourced technology provider to the restaurant and we're helping them

Speaker A: to better understand their guests.

Speaker E: It's a really relevant point and one that people don't think about very much.

Speaker A: But when an order goes through a

Speaker E: doordash or an Uber Eats or any other third party marketplace, the restaurant gets zero data. They don't know who that guest is. They can't tell, is this in incremental to my business or is this one of my existing customers? It is a low profit or no profit transaction. And so the restaurant is flying blind without instruments.

Speaker A: When an order is coming through olo,

Speaker E: we are able to Share the data back to the restaurant about who this guest is in real time. Help them to personalize that interaction, that transaction itself. Make a smart recommendation to them, as you saw in the video. And again, as a network where we can see that guest across multiple brands, we can enrich what the restaurant brand can see in its own silo. And we can give them clues about things that they should be recommending to the guest based on what we know about the guest. So that is a very powerful alignment. It's something that I know. Our chairman, Dave Wagner, who's the CEO of Everbridge and was, uh, a brilliant pick by Thoma Bravo as our chairman, he's always been fascinated with this alignment of when your customers win, you win, and that these things compound together. And when I talk about the massive tailwind that AI represents, at the same time these things compound together. And that's a great positioning.

Speaker C: Sure. As you think about the vision for the next four years partnership with Thoma Bravo, what's the one thing that gets you the most excited in the business?

Speaker E: Yeah, look, everything that you mentioned of how to convince me that this was a great path, um, has turned out to be true. Um, I am so grateful sitting here two weeks after our first board meeting. I was reflecting earlier at lunch. I've never felt as aligned with my board coming out of a board meeting for 20 years. It was really an incredible thing to know. We know our industry, we know our customers, we know it really well. Thoma Bravo understands pattern recognition of how to grow and scale software companies across 500 portfolio companies over 30 years. And matching those two skill sets together, um, there's just a humility and a respect that each side has for the other. That was super, super refreshing. Um, we're moving quickly, we're making decisions quickly as a board. Acquisitions, um, are exciting, green lighting new product ideas that are going to be transformative. Um, that's really exciting. I think the thing that I've hinted at a couple times that I'm most excited about is this network effect of Olo as provider to about 65 of the top 100 restaurant brands. Um, seeing about 100 million guests per year. And now with the ability to see that guest at the Olo platform level. So we have about 20 million plus guests that have an account with ologies that they can then use across our platform. And that gives us incredible, I call them networked data rights. So I'm really excited about what that sets up. Nothing to announce, uh, here specifically today, but something that we're excited about. Internally talked about in our board meeting two weeks ago. I, uh, reinforced with the team last week and one uh, thing I said to my team always is I'm hugely grateful and I'm unsatisfied and those two things can live together. I'm grateful for the past 20 years

Speaker A: and all of the hard work and

Speaker E: all the good fortune that we've had that have taken us to this point. But I don't think that we are at uh, a summit. I think that we are at base camp and there is a beautiful, beautiful climb ahead of us and it's not going to be easy, but it's going to be epic and we're going to be very proud of the result. Um, I am so excited about our future as a company and particularly with Thoma Bravo as our partner. And uh, I asked and you told me I could share this, um, sat down with M, my wife and our 8 year old son shortly after, uh, the transaction closed. As you know, we've reinvested 75% of our proceeds back into the company. Um, and couldn't be more thrilled to do that because I believe we're in a transformative moment for our industry and for our company.

Speaker C: It's good to hear good alignment with the people in this room. What kind of conversations are you having with restaurant customers about AI? What are they asking you for? Uh, is the interaction changing at all with your customers?

Speaker E: Yeah, I agree with some of the comments earlier. I forget who said it exactly. It was sort of a curiosity at first or maybe something that it felt like we were foisting upon them. Now we're being pulled into opportunities and oftentimes it's the more obvious and sexy opportunities. Like could you develop a voice AI interface for phone calls that still come into the restaurant or the drive thru. What we found that are maybe the most high impact investments and offerings that we've built in AI to date are things that are less sexy. Like predicting how much capacity the kitchen has at the current time based on all the orders that we can see through the platform and understanding if Hudson's ordering right now is going to take 10 minutes or is it really going to take 20 minutes. And if you told him it was going to be ready in 10 minutes, he would come and be frustrated. Waiting there for 10 minutes and you're stressing out your kitchen or throttling orders. So throttling orders and understanding quote times and um, then communicating that out to a network of delivery providers that's critically important. We do that through machine learning and AI. Um, for brands like Cheesecake Factory who have incredibly high intensity kitchens. Um, and the goal is always keep them as productive as possible and as profitable as possible. And with AI, we can really make that happen.

Speaker C: What about internal use cases? Any internal AI efficiency use cases yet?

Speaker E: Yeah, across every department. I mean, and I'll tell you right now, concurrent with this event, uh, our engineering team and really our whole team, they've invited everybody to join, is engaged in an AI themed hackathon. And they were just chomping at the bit to do this. They're so excited about building things out over the course of this week and then doing presentations on Friday. Um, and I'm very excited to see what comes of that. But we're using AI. The engineering team is using AI, but also go to market is using AI. We're big believers in Clari. That was a great point of alignment with our operating partners as we kind of got ready for that first board meeting two weeks ago and on an ongoing basis. Um, but we're using it extensively throughout the company.

Speaker C: That's great. I think we're at time for, uh, some questions, if we have any.

Speaker F: Great. Maybe we'll turn to Slido. Um, how will AI affect employment in the restaurant industry as OLO has the data points to provide a more efficient experience.

Speaker A: I'll give you a great case study. Um, not necessarily in AI, but just in technology and how it's impacting jobs. So this was from our customer conference. It was, uh, two years ago now. Uh, we had Danny Meyer, who is a longtime board member at olo, founder of Shake Shack. Uh, people here probably know him for that. And a lot of high end New York restaurants. Um, at Shake Shack, they converted what was a standard three cashier lanes into kiosk lanes. And what they did was, he said, you know, we did not all of a sudden lower the number of roles we were staffing for by three. What we did was we kept one cashier there and that person would sort of act as a cashier and we'd spin the kiosk around and they would take an order in the traditional way. We had another person who was no longer a cashier. We called them a, ah, hospitality helper. He had some name like that. And they would come out and if people needed a little bit of help to show them how to use the kiosk, that was their role. And then we did eliminate one of those roles. So I think that's a good illustration and I think it's a good anecdote. Um, I don't see this removing humans and the human layer of hospitality, uh, in totality from restaurants. What I see it doing is helping the humans in the restaurant really do the human things and deliver hospitality. I think it's much nicer to hand somebody their order and smell, smile and talk about what they ordered and what they might like next time or give them a free sample of something that you know they're going to like because of the massive data set than it is to be taking their order, mis entering it, making change for them. Those are kind of rote tasks that technology is better at doing, AI included than uh, humans. And humans should really deliver that human layer of genuine connection with guests.

Speaker F: That's great. Why are the POS systems or kitchen systems not able to or not trying to accomplish some of these same goals?

Speaker A: So POS was really built as a staff facing technology. It is the ability for the staff to build up an order, uh, on behalf of the guest, take payment on behalf of the guest. It is inherently not a guest centric technology. Digital ordering with its origins in, in E commerce, that is an inherently guest centric technology. And so that's sort of how we think about our role as the guest facing tech stack versus the point of sales role as the staff facing tech stack. Fun fact. We do have now I know of two brands that are customers of OLO who are emerging brands launching, but they're launching without a traditional pos. They are using OLO full stack and they're connecting in other software services into OLO for things like purchasing and inventory and labor and scheduling. Things that aren't inherently guest facing technologies, not things that we do. I've uh, neglected to mention this, but we have a network of 400 technology partners as an open platform and the largest open platform of its kind that plug into OLO and a lot of them do that kind of back of house staff facing technology sort of thing. Now we have these restaurant brands showing the way that the future will be, I believe, a future that does not involve the POs as we've come to know it. Because if you can use AI to order or use your own interface to order and have accuracy go up and speed go up and convenience in general go up, why would a restaurant insist that you go and speak to somebody to mishear your order, mis enter your order and then send it back to the kitchen when you can just skip that hop and go straight from the guest interface directly to the kitchen interface

Speaker C: on your phone interface as well?

Speaker E: Yep.

Speaker F: Yeah, that's great. Um, Noah, you've Lived through many multiple technology hype cycles. Looking out over the next decade, what gives you confidence that the core economics of software, recurring revenue, scalability and high margins remain intact even as AI reshapes how products are built and delivered?

Speaker A: I guess my main confidence comes from the fact that over 20 years of operating the company, we've been so aligned with restaurants and adding so much value to restaurants. And we've seen restaurants, we've experienced brands leaving the platform and trying to build something on their own to replicate what we do. And we love those stories because they all wind up as what we call boomerangs where they leave and then they come back because they realize it's not just really expensive to build software, it's really expensive to operate software. It's not the capex, it's the opex. When we look at what big, big brands who built before we were even a company and an option for them to consider are spending on this, without exception, it is more than double on a per transaction basis what our platform costs. And so I think we have such an inherent advantage as a SaaS platform at scale that we can deliver a world class product at a fraction of the cost. And that has been true for our entire history. And it becomes more and more true as we grow in scale and offer more capabilities to our customers.

Speaker F: It already feels like restaurants push you out the door. How do you ensure that this efficiency doesn't affect the experience?

Speaker A: I think restaurants also have to choose. How much of this do they want to do? We've come a little bit into this future, um, but there are still restaurants that are doing some things like this today. Um, some fine dining restaurants really don't want it to feel automated or enriched by technology at all. But a big part of the inspiration for some of the experiences you saw there is from studying really fine dining restaurants. And one in particular, uh, 11 Madison park in New York. So one of the keynote speakers was a guy named Will Guidera, uh, um, who was one of the owners in the front of the house maitre D at 11, uh Madison Park. And a lot of the things that they did to become the number one restaurant in the world were things that we drew inspiration from in building out

Speaker E: this vision and a lot of things

Speaker A: that Danny Meyer did with keeping track of guests and keeping track of their preferences and remembering them and using technology to remind the hostess stand, the servers, who this guest was, what they liked, when they were last in which restaurant they came to. All of those were kind of the source material that we then used to Go and build out a lot of these capabilities or that have inspired future capabilities. So I think even the finest dining restaurants see attributes of what we're doing that they can apply to augment the hospitality they offer. And not every restaurant is going to put kiosks in. That's appropriate for some restaurants, it's not appropriate for others. Just like not every restaurant has a drive through. I think there's a lot of different ways to play here. But what is fundamentally true of every restaurant is they want their guests to feel known, to feel seen, to feel like they remember them and are personalizing their experience. And that is their best way for time immemorial to grow guest lifetime value and increase the number of times that a guest comes back to their restaurant. And that's everything that we do in software form.

Speaker F: Great. Maybe one last question. Do you find that restaurant chains are concerned that you might empower their competitors with data from people they consider to be their customers?

Speaker A: I think that there is a span of perspectives on that. Um, but I'll say that this is not happening in a vacuum. There are these marketplaces like Doordash, like Uber Eats, that are a growing percentage of overall transactions where the restaurant is not only in one place with all of their competitors, but when an order comes in, they don't know who that guest is and they effectively or their franchisees make no money on the transaction. And an alternative where restaurants can kind of have strength in numbers and band together to better serve their guests when they come in by sharing data about their guests across different experiences is something that our restaurants are asking for. They're saying, can OLO step into this role as the scale platform, as a network of restaurants to help us fight back against these third party marketplaces. It's very similar to hotels versus OTAs or airlines versus OTAs from a decade ago. The direct relationship with the guest is essential. And that's what we're aligned to help restaurants to do. And they're, um, asking us to do more of it. And over the years, the best product launches we've ever had are when we're hearing from our customers, from our product advisory council. We need help. Can you help us? And the answer is yes. And we go and build something for them.

Speaker C: And most restaurant chains don't have large IT staffs to do this.

Speaker E: No, no.

Speaker A: Um, restaurants are not only shrinking the

Speaker E: number of people inside the restaurant, they're shrinking the number of people at headquarters to get profitability at the franchise level, at the franchisor level.

Speaker F: That's right. Great.

Speaker C: Thank you very much, Noah.

Speaker E: Thank you.

Speaker A: Thanks, everybody.

Speaker B: Listen to Thoma Bravo's behind The Deal, Season 4 on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts.